def test_fast__DeepLIFTWrapper_serialize():
def method(model):
return DeepLIFTWrapper(model)
with pytest.raises(AssertionError):
# Issue in deeplift.
dryrun.test_serialize_analyzer(method, "trivia.*:mnist.log_reg")
def test_fast__SerializePatternNet():
def method(model):
# enough for test purposes, only pattern application is tested here
# pattern computation is tested separately.
# assume that one dim weights are biases, drop them.
patterns = [x for x in model.get_weights() if len(x.shape) > 1]
return PatternNet(model, patterns=patterns)
dryrun.test_serialize_analyzer(method, "mnist.log_reg")
def test_fast__DeepLIFT_serialize():
def method(model):
return DeepLIFT(model)
dryrun.test_serialize_analyzer(method, "trivia.*:mnist.log_reg")
def test_fast__SerializeRandom():
def method(model):
return Random(model)
dryrun.test_serialize_analyzer(method, "trivia.*:mnist.log_reg")
def test_fast__SerializeReverseAnalyzerkBase():
def method(model):
return Gradient(model)
dryrun.test_serialize_analyzer(method, "trivia.*:mnist.log_reg")
def test_fast__SerializeLRPAlpha2Beta1():
def method(model):
return LRPAlpha2Beta1(model)
dryrun.test_serialize_analyzer(method, "trivia.*:mnist.log_reg")
def test_fast__SerializeAugmentReduceBase():
def method(model):
return AugmentReduceBase(Gradient(model))
dryrun.test_serialize_analyzer(method, "trivia.*:mnist.log_reg")
def test_fast__SerializePathIntegrator():
def method(model):
return PathIntegrator(Gradient(model))
dryrun.test_serialize_analyzer(method, "trivia.*:mnist.log_reg")
def test_fast__SerializeGaussianSmoother():
def method(model):
return GaussianSmoother(Gradient(model))
dryrun.test_serialize_analyzer(method, "trivia.*:mnist.log_reg")
